Tag Archives: machine learning

climate change, technology forecasting

Links – early August, 2015

Well, I’m back, after deep dives into R programming and statistical modeling. I’d like to offer these links which I’ve bookmarked in recent days. The first four cover a scatter of topics, from impacts of the so-called sharing economy and climate developments to the currency impacts of the more and more certain moves by the US Federal Reserve to increase interest rates in September.

But then I’ve collected a number of useful links on robotics and artificial intelligence.

How the ‘sharing economy’ is upending the travel industry

DS: New York Attorney General Eric Schneiderman last October issued a report finding 72 percent of the reservations on Airbnb going back to 2010 were in violation of city law. What’s the industry doing to address these concerns?

MB: Listen, I think there are a lot of outdated regulations and a lot of outdated laws that were written in a time where you couldn’t possibly imagine the innovation that has come up from the sharing economy, and a lot of those need to be updated to meet the world that we live in today, and I think that’s important.  Sometimes you have regulations that are put in place by incumbent industries that didn’t want competition and you have some regulations that were put in place back in the ’60s and ’70s, where you couldn’t imagine any of these things, and so I think sometimes you need to see updates.

So there you go – laws on the books are outdated.

Brain-controlled prosthesis nearly as good as one-finger typing

The goal of all this research is to get thought-controlled prosthetics to people with ALS. Today these people may use an eye-tracking system to direct cursors or a “head mouse” that tracks the movement of the head. Both are fatiguing to use. Neither provides the natural and intuitive control of readings taken directly from the brain.

The U.S. Food and Drug Administration recently gave Shenoy’s team the green light to conduct a pilot clinical trial of their thought-controlled cursor on people with spinal cord injuries.

Jimmy Carter: The U.S. Is an “Oligarchy With Unlimited Political Bribery”

Unfortunately, very apt characterization from a formal standpoint of political science.

Carter

What to Expect from El Niño: North America

The only El Niño events in NOAA’s 1950-2015 database comparable in strength to the one now developing occurred in 1982-83 and 1997-98… Like other strong El Niño events, this one will almost certainly last just one winter. But at least for the coming wet season, it holds encouraging odds of well-above average precipitation for California. During a strong El Niño, the subtropical jet stream is energized across the southern U.S., while the polar jet stream tends to stay north of its usual winter position or else consolidate with the subtropical jet. This gives warm, wet Pacific systems a better chance to push northeast into California… Milder and drier a good bet for Pacific Northwest, Northern Plains, western Canada.. Rockies snowfall: The south usually wins out…Thanks to the jet-shifting effects noted above, snowfall tends to be below average in the Northern Rockies and above average in the Southern Rockies during strong El Niños. The north-south split extends to Colorado, where northern resorts such as Steamboat Springs typically lose out to areas like the San Juan and Sangre de Cristo ranges across the southern part of the state. Along the populous Front Range from Denver to Fort Collins, El Niño hikes the odds of a big snowstorm, especially in the spring and autumn. About half of Boulder’s 12” – 14” storms occur during El Niño, and the odds of a 20” or greater storm are quadrupled during El Niño as opposed to La Niña.

According to NOAA, the single most reliable El Niño outcome in the United States, occurring in more than 80% of El Niño events over the last century, is the tendency for wet wintertime conditions along and near the Gulf Coast, thanks to the juiced-up subtropical jet stream.

Emerging market currencies crash on Fed fears and China slump

The currencies of Brazil, Mexico, South Africa and Turkey have all crashed to multi-year lows as investors flee emerging markets and commodity prices crumble.

Robotics and Artificial Intelligence

Some of the most valuable research I’ve found so far on the job and societal impacts of robotics comes from a survey of experts conducted by the Pew Research Internet Project AI, Robotics, and the Future of Jobs,

Some 1,896 experts responded to the following question:

The economic impact of robotic advances and AI—Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?

Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.

The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution.

Read this – the comments on both sides of this important question are trenchant, important.

The next most useful research comes from a 2011 publication of Brian Arthur in the McKinsey Quarterly The second economy – which is the part of the economy where machines transact just with other machines.

Something deep is going on with information technology, something that goes well beyond the use of computers, social media, and commerce on the Internet. Business processes that once took place among human beings are now being executed electronically. They are taking place in an unseen domain that is strictly digital. On the surface, this shift doesn’t seem particularly consequential—it’s almost something we take for granted. But I believe it is causing a revolution no less important and dramatic than that of the railroads. It is quietly creating a second economy, a digital one.

Twenty years ago, if you went into an airport you would walk up to a counter and present paper tickets to a human being. That person would register you on a computer, notify the flight you’d arrived, and check your luggage in. All this was done by humans. Today, you walk into an airport and look for a machine. You put in a frequent-flier card or credit card, and it takes just three or four seconds to get back a boarding pass, receipt, and luggage tag. What interests me is what happens in those three or four seconds. The moment the card goes in, you are starting a huge conversation conducted entirely among machines. Once your name is recognized, computers are checking your flight status with the airlines, your past travel history, your name with the TSA (and possibly also with the National Security Agency). They are checking your seat choice, your frequent-flier status, and your access to lounges. This unseen, underground conversation is happening among multiple servers talking to other servers, talking to satellites that are talking to computers (possibly in London, where you’re going), and checking with passport control, with foreign immigration, with ongoing connecting flights. And to make sure the aircraft’s weight distribution is fine, the machines are also starting to adjust the passenger count and seating according to whether the fuselage is loaded more heavily at the front or back.

These large and fairly complicated conversations that you’ve triggered occur entirely among things remotely talking to other things: servers, switches, routers, and other Internet and telecommunications devices, updating and shuttling information back and forth. All of this occurs in the few seconds it takes to get your boarding pass back. And even after that happens, if you could see these conversations as flashing lights, they’d still be flashing all over the country for some time, perhaps talking to the flight controllers—starting to say that the flight’s getting ready for departure and to prepare for that…

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent—a great computer expression—which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing…

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent—a great computer expression—which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing

I’m interested in how to measure the value of services produced in this “second economy.”

Finally, China’s adoption of robotics seems to signal something – as in this piece about a totally automatic factor for cell phone parts –

China sets up first unmanned factory; all processes are operated by robots

At the workshop of Changying Precision Technology Company in Dongguan, known as the “world factory”, which manufactures cell phone modules, 60 robot arms at 10 production lines polish the modules day .. The technical staff just sits at the computer and monitors through a central control system… In the plant, all the processes are operated by computer- controlled robots, computer numerical control machining equipment, unmanned transport trucks and automated warehouse equipment.

automatic forecasting software, predictive analytics, stock trading algorithms

High Frequency Trading – 2

High Frequency Trading (HFT) occurs faster than human response times – often quoted as 750 milliseconds. It is machine or algorithmic trading, as Sean Gourley’s “High Frequency Trading and the New Algorithmic Ecosystem” highlights.

This is a useful introductory video.

It mentions Fixnetix’s field programmable array chip and new undersea cables designed to shave milliseconds off trading speeds from Europe to the US and elsewhere.

Also, Gourley refers to dark pool pinging, which tries to determine the state of large institutional orders by “sniffing them out” and using this knowledge to make (almost) risk-free arbitrage by trading on different exchanges in milliseconds or faster. Institutional investors using slower and not-so-smart algorithms lose.

Other HFT tractics include “quote stuffing”, “smoking”, and “spoofing.” Of these, stuffing may be the most damaging. It limits access of slower traders by submitting large numbers of orders and then canceling them very quickly. This leads to order congestion, which may create technical trouble and lagging quotes.

Smoking and spoofing strategies, on the other hand, try to manipulate other traders to participate in trading at unfavorable moments, such as just before the arrival of relevant news.

Here are some more useful links on this important development and the technological arms race that has unfolded around it.

Financial black swans driven by ultrafast machine ecology Key research on ultrafast black swan events

Nanosecond Trading Could Make Markets Go Haywire Excellent Wired article

High-Frequency Trading and Price Discovery

Defense of HFT on basis that HFTs’ trade (buy or sell) in the direction of permanent price changes and against transitory pricing errors creates benefits which outweigh adverse selection of HFT liquidity supplying (non-marketable) limit orders.

The Good, the Bad, and the Ugly of Automated High-Frequency Trading tries to strike a balance, but tilts toward a critique

Has HFT seen its heyday? I read at one and the same time I read at one and the same time that HFT profits per trade are dropping, that some High Frequency Trading companies report lower profits or are shutting their doors, but that 70 percent of the trades on the New York Stock Exchange are the result of high frequency trading.

My guess is that HFT is a force to be dealt with, and if financial regulators are put under restraint by the new US Congress, we may see exotic new forms flourishing in this area. 

Bayesian methods, data science, machine learning, natural language processing, predictive analytics, Uncategorized

Video Friday – Andrew Ng’s Machine Learning Course

Well, I signed up for Andrew Ng’s Machine Learning Course at Stanford. It began a few weeks ago, and is a next generation to lectures by Ng circulating on YouTube. I’m going to basically audit the course, since I started a little late, but I plan to take several of the exams and work up a few of the projects. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. I like the change in format. The YouTube videos circulating on the web are lengthly, and involve Ng doing derivations on white boards. This is a more informal, expository format. Here is a link to a great short introduction to neural networks. Ngrobot Click on the link above this picture, since the picture itself does not trigger a YouTube. Ng’s introduction on this topic is fairly short, so here is the follow-on lecture, which starts the task of representing or modeling neural networks. I really like the way Ng approaches this is grounded in biology. I believe there is still time to sign up. Comment on Neural Networks and Machine Learning I can’t do much better than point to Professor Ng’s definition of machine learning – Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. And now maybe this is the future – the robot rock band.

artificial intelligence (AI), celebrity forecasters, Chinese economy, climate change, crowdsourcing analytics, data science, extreme weather forecasting, geopolitical risk, machine learning

Links – early July 2014

While I dig deeper on the current business outlook and one or two other issues, here are some links for this pre-Fourth of July week.

Predictive Analytics

A bunch of papers about the widsom of smaller, smarter crowds I think the most interesting of these (which I can readily access) is Identifying Expertise to Extract the Wisdom of Crowds which develops a way by eliminating poorly performing individuals from the crowd to improve the group response.

Application of Predictive Analytics in Customer Relationship Management: A Literature Review and Classification From the Proceedings of the Southern Association for Information Systems Conference, Macon, GA, USA March 21st–22nd, 2014. Some minor problems with writing English in the article, but solid contribution.

US and Global Economy

Nouriel Roubini: There’s ‘schizophrenia’ between what stock and bond markets tell you Stocks tell you one thing, but bond yields suggest another. Currently, Roubini is guardedly optimistic – Eurozone breakup risks are receding, US fiscal policy is in better order, and Japan’s aggressively expansionist fiscal policy keeps deflation at bay. On the other hand, there’s the chance of a hard landing in China, trouble in emerging markets, geopolitical risks (Ukraine), and growing nationalist tendencies in Asia (India). Great list, and worthwhile following the links.

The four stages of Chinese growth Michael Pettis was ahead of the game on debt and China in recent years and is now calling for reduction in Chinese growth to around 3-4 percent annually.

Because of rapidly approaching debt constraints China cannot continue what I characterize as the set of “investment overshooting” economic polices for much longer (my instinct suggests perhaps three or four years at most). Under these policies, any growth above some level – and I would argue that GDP growth of anything above 3-4% implies almost automatically that “investment overshooting” policies are still driving growth, at least to some extent – requires an unsustainable increase in debt. Of course the longer this kind of growth continues, the greater the risk that China reaches debt capacity constraints, in which case the country faces a chaotic economic adjustment.

Politics

Is This the Worst Congress Ever? Barry Ritholtz decries the failure of Congress to lower interest rates on student loans, observing –

As of July 1, interest on new student loans rises to 4.66 percent from 3.86 percent last year, with future rates potentially increasing even more. This comes as interest rates on mortgages and other consumer credit hovered near record lows. For a comparison, the rate on the 10-year Treasury is 2.6 percent. Congress could have imposed lower limits on student-loan rates, but chose not to.

This is but one example out of thousands of an inability to perform the basic duties, which includes helping to educate the next generation of leaders and productive citizens. It goes far beyond partisanship; it is a matter of lack of will, intelligence and ability.

Hear, hear.

Climate Change

Climate news: Arctic seafloor methane release is double previous estimates, and why that matters This is a ticking time bomb. Article has a great graphic (shown below) which contrasts the projections of loss of Artic sea ice with what actually is happening – underlining that the facts on the ground are outrunning the computer models. Methane has more than an order of magnitude more global warming impact that carbon dioxide, per equivalent mass.

ArcticSeaIce

Dahr Jamail | Former NASA Chief Scientist: “We’re Effectively Taking a Sledgehammer to the Climate System”

I think the sea level rise is the most concerning. Not because it’s the biggest threat, although it is an enormous threat, but because it is the most irrefutable outcome of the ice loss. We can debate about what the loss of sea ice would mean for ocean circulation. We can debate what a warming Arctic means for global and regional climate. But there’s no question what an added meter or two of sea level rise coming from the Greenland ice sheet would mean for coastal regions. It’s very straightforward.

Machine Learning

EG

Computer simulating 13-year-old boy becomes first to pass Turing test A milestone – “Eugene Goostman” fooled more than a third of the Royal Society testers into thinking they were texting with a human being, during a series of five minute keyboard conversations.

The Milky Way Project: Leveraging Citizen Science and Machine Learning to Detect Interstellar Bubbles Combines Big Data and crowdsourcing.

artificial intelligence (AI), natural language processing

The Loebner Prize and Turing Test

In a brilliant, early article on whether machines can “think,” Alan Turing, the genius behind a lot of early computer science, suggested that if a machine cannot be distinguished from a human during text-based conversation, that machine could be said to be thinking and have intelligence.

Every year, the Loebner Prize holds this type of Turing comptition. Judges, such as those below, interact with computer programs (and real people posing as computer programs). If a

judges

computer program fools enough people, the program is elible for various prizes.

The 2013 prize was won by Mitsuki Chatbox advertised as an artificial lifeform living on the web.

chatbot

She certainly is fun, and is an avatar of a whole range of chatbots which are increasingly employed in customer service and other business applications.

Mitsuku’s botmaster, Steve Worswick, ran a music website with a chatbot. Apparently, more people visited to chat than for music so he concentrated his efforts on the bot, which he still regards as a hobby. Mitsuku uses AIML (Artificial Intelligence Markup Language) used by members of pandorabot.

Mitsuki is very cute, which perhaps one reason why she gets worldwide attention.

pageviews

It would be fun to develop a forecastbot, capable of answering basic questions about which forecasting method might be appropriate. We’ve all seen those flowcharts and tabular arrays with data characteristics and forecast objectives on one side, and recommended methods on the other.

 

accuracy of forecasts, Big Data, boosting, macroeconomic forecasting, time series forecasting, yield curve

Leading Indicators

One value the forecasting community can provide is to report on the predictive power of various leading indicators for key economic and business series.

The Conference Board Leading Indicators

The Conference Board, a private, nonprofit organization with business membership, develops and publishes leading indicator indexes (LEI) for major national economies. Their involvement began in 1995, when they took over maintaining Business Cycle Indicators (BCI) from the US Department of Commerce.

For the United States, the index of leading indicators is based on ten variables: average weekly hours, manufacturing,  average weekly initial claims for unemployment insurance, manufacturers’ new orders, consumer goods and materials, vendor performance, slower deliveries diffusion index,manufacturers’ new orders, nondefense capital goods, building permits, new private housing units, stock prices, 500 common stocks, money supply, interest rate spread, and an index of consumer expectations.

The Conference Board, of course, also maintains coincident and lagging indicators of the business cycle.

This list has been imprinted on the financial and business media mind, and is a convenient go-to, when a commentator wants to talk about what’s coming in the markets. And it used to be that a rule of thumb that three consecutive declines in the Index of Leading Indicators over three months signals a coming recession. This rule over-predicts, however, and obviously, given the track record of economists for the past several decades, these Conference Board leading indicators have questionable predictive power.

Serena Ng Research

What does work then?

Obviously, there is lots of research on this question, but, for my money, among the most comprehensive and coherent is that of Serena Ng, writing at times with various co-authors.

SerenaNg

So in this regard, I recommend two recent papers

Boosting Recessions

Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling

The first paper is most recent, and is a talk presented before the Canadian Economic Association (State of the Art Lecture).

Hallmarks of a Serena Ng paper are coherent and often quite readable explanations of what you might call the Big Picture, coupled with ambitious and useful computation – usually reporting metrics of predictive accuracy.

Professor Ng and her co-researchers apparently have determined several important facts about predicting recessions and turning points in the business cycle.

For example –

  1. Since World War II, and in particular, over the period from the 1970’s to the present, there have been different kinds of recessions. Following Ng and Wright, ..business cycles of the 1970s and early 80s are widely believed to be due to supply shocks and/or monetary policy. The three recessions since 1985, on the other hand, originate from the financial sector with the Great Recession of 2008-2009 being a full-blown balance sheet recession. A balance sheet recession involves, a sharp increase in leverage leaves the economy vulnerable to small shocks because, once asset prices begin to fall, financial institutions, firms, and households all attempt to deleverage. But with all agents trying to increase savings simultaneously, the economy loses demand, further lowering asset prices and frustrating the attempt to repair balance sheets. Financial institutions seek to deleverage, lowering the supply of credit. Households and firms seek to deleverage, lowering the demand for credit.
  2. Examining a monthly panel of 132 macroeconomic and financial time series for the period 1960-2011, Ng and her co-researchers find that .. the predictor set with systematic and important predictive power consists of only 10 or so variables. It is reassuring that most variables in the list are already known to be useful, though some less obvious variables are also identified. The main finding is that there is substantial time variation in the size and composition of the relevant predictor set, and even the predictive power of term and risky spreads are recession specific. The full sample estimates and rolling regressions give confidence to the 5yr spread, the Aaa and CP spreads (relative to the Fed funds rate) as the best predictors of recessions.

So, the yield curve, a old favorite when it comes to forecasting recessions or turning points in the business cycle, performs less well in the contemporary context – although other (limited) research suggests that indicators combining facts about the yield curve with other metrics might be helpful.

And this exercise shows that the predictor set for various business cycles changes over time, although there are a few predictors that stand out. Again,

there are fewer than ten important predictors and the identity of these variables change with the forecast horizon. There is a distinct difference in the size and composition of the relevant predictor set before and after mid-1980. Rolling window estimation reveals that the importance of the term and default spreads are recession specific. The Aaa spread is the most robust predictor of recessions three and six months ahead, while the risky bond and 5yr spreads are important for twelve months ahead predictions. Certain employment variables have predictive power for the two most recent recessions when the interest rate spreads were uninformative. Warning signals for the post 1990 recessions have been sporadic and easy to miss.

Let me throw in my two bits here, before going on in subsequent posts to consider turning points in stock markets and in more micro-focused or industry time series.

At the end of “Boosting Recessions” Professor Ng suggests that higher frequency data may be a promising area for research in this field.

My guess is that is true, and that, more and more, Big Data and data analytics from machine learning will be applied to larger and more diverse sets of macroeconomics and business data, at various frequencies.

This is tough stuff, because more information is available today than in, say, the 1970’s or 1980’s. But I think we know what type of recession is coming – it is some type of bursting of the various global bubbles in stock markets, real estate, and possibly sovereign debt. So maybe more recent data will be highly relevant.

artificial intelligence (AI), Big Data, robotics, technology forecasting

Jobs and the Next Wave of Computerization

A duo of researchers from Oxford University (Frey and Osborne) made a splash with their analysis of employment and computerization in the US (English spelling). Their research, released September of last year, projects that –

47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two..

Based on US Bureau of Labor Statistics (BLS) classifications from O*NET Online, their model predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are at risk.

This research deserves attention, if for no other reason than masterful discussions of the impact of technology on employment and many specific examples of new areas for computerization and automation.

For example, I did not know,

Oncologists at Memorial Sloan-Kettering Cancer Center are, for example, using IBM’s Watson computer to provide chronic care and cancer treatment diagnostics. Knowledge from 600,000 medical evidence reports, 1.5 million patient records and clinical trials, and two million pages of text from medical journals, are used for benchmarking and pattern recognition purposes. This allows the computer to compare each patient’s individual symptoms, genetics, family and medication history, etc., to diagnose and develop a treatment plan with the highest probability of success..

There are also specifics of computerized condition monitoring and novelty detection -substituting for closed-circuit TV operators, workers examining equipment defects, and clinical staff in intensive care units.

A followup Atlantic Monthly article – What Jobs Will the Robots Take? – writes,

We might be on the edge of a breakthrough moment in robotics and artificial intelligence. Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats. Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they’re bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do. This is why some people think managers are, for the moment, one of the largest categories immune to the rushing wave of AI.

Meanwhile, lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can’t answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.

What Frey and Osborne at Oxford suggest is an inflection point, where machine learning (ML) and what they call mobile robotics (MR) have advanced to the point where new areas for applications will open up – including a lot of menial, service tasks that were not sufficiently routinized for the first wave.

In addition, artificial intelligence (AI) and Big Data algorithms are prying open up areas formerly dominated by intellectual workers.

The Atlantic Monthly article cited above has an interesting graphic –

jobsautomationSo at the top of this chart are the jobs which are at 100 percent risk of being automated, while at the bottom are jobs which probably will never be automated (although I do think counseling can be done to a certain degree by AI applications).

The Final Frontier

This blog focuses on many of the relevant techniques in machine learning – basically unsupervised learning of patterns – which in the future will change everything.

Driverless cars are the wow example, of course.

Bottlenecks to moving further up the curve of computerization are highlighted in the following table from the Oxford U report.

ONETvars

As far as dexterity and flexibility goes, Baxter shows great promise, as the following YouTube from his innovators illustrates.

There also are some wonderful examples of apparent creativity by computers or automatic systems, which I plan to detail in a future post.

Frey and Osborn, reflecting on their research in a 2014 discussion, conclude

So, if a computer can drive better than you, respond to requests as well as you and track down information better than you, what tasks will be left for labour? Our research suggests that human social intelligence and creativity are the domains were labour will still have a comparative advantage. Not least, because these are domains where computers complement our abilities rather than substitute for them. This is because creativity and social intelligence is embedded in human values, meaning that computers would not only have to become better, but also increasingly human, to substitute for labour performing such work.

Our findings thus imply that as technology races ahead, low-skill workers will need to reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills. Development strategies thus ought to leverage the complementarity between computer capital and creativity by helping workers transition into new work, involving working with computers and creative and social ways.

Specifically, we recommend investing in transferable computer-related skills that are not particular to specific businesses or industries. Examples of such skills are computer programming and statistical modeling. These skills are used in a wide range of industries and occupations, spanning from the financial sector, to business services and ICT.

Implications For Business Forecasting

People specializing in forecasting for enterprise level business have some responsibility to “get ahead of the curve” – conceptually, at least.

Not everybody feels comfortable doing this, I realize.

However, I’m coming to the realization that these discussions of how many jobs are susceptible to “automation” or whatever you want to call it (not to mention jobs at risk for “offshoring”) – these discussions are really kind of the canary in the coal mine.

Something is definitely going on here.

But what are the metrics? Can you backdate the analysis Frey and Osborne offer, for example, to account for the coupling of productivity growth and slower employment gains since the last recession?

Getting a handle on this dynamic in the US, Europe, and even China has huge implications for marketing, and, indeed, social control.

asset bubbles, Big Data, Chinese economy, data mining, data science, developing country forecast, forecasting asset bubbles, global business forecasts, IBM Watson, machine learning, macroeconomic forecasting, predictive analytics

Links – February 1, 2014

IT and Big Data

Kayak and Big Data Kayak is adding prediction of prices of flights over the coming 7 days to its meta search engine for the travel industry.

China’s Lenovo steps into ring against Samsung with Motorola deal Lenovo Group, the Chinese technology company that earns about 80 percent of its revenue from personal computers, is betting it can also be a challenger to Samsung Electronics Co Ltd and Apple Inc in the smartphone market.

5 Things To Know About Cognitive Systems and IBM Watson Rob High video on Watson at http://www.redbooks.ibm.com/redbooks.nsf/pages/watson?Open. Valuable to review. Watson is probably different than you think. Deep natural language processing.

Playing Computer Games and Winning with Artificial Intelligence (Deep Learning) Pesents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards… [applies] method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm…outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

Global Economy

China factory output points to Q1 lull Chinese manufacturing activity slipped to its lowest level in six months, with indications of slowing growth for the quarter to come in the world’s second-largest economy.

Japan inflation rises to a 5 year high, output rebounds Japan’s core consumer inflation rose at the fastest pace in more than five years in December and the job market improved, encouraging signs for the Bank of Japan as it seeks to vanquish deflation with aggressive money printing.

Coup Forecasts for 2014

coupforecast                       

World risks deflationary shock as BRICS puncture credit bubbles Ambrose Evans-Pritchard does some nice analysis in this piece.

Former IMF Chief Economist, Now India’s Central Bank Governor Rajan Takes Shot at Bernanke’s Destabilizing Policies

Some of his key points:

Emerging markets were hurt both by the easy money which flowed into their economies and made it easier to forget about the necessary reforms, the necessary fiscal actions that had to be taken, on top of the fact that emerging markets tried to support global growth by huge fiscal and monetary stimulus across the emerging markets. This easy money, which overlaid already strong fiscal stimulus from these countries. The reason emerging markets were unhappy with this easy money is “This is going to make it difficult for us to do the necessary adjustment.” And the industrial countries at this point said, “What do you want us to do, we have weak economies, we’ll do whatever we need to do. Let the money flow.”

Now when they are withdrawing that money, they are saying, “You complained when it went in. Why should you complain when it went out?” And we complain for the same reason when it goes out as when it goes in: it distorts our economies, and the money coming in made it more difficult for us to do the adjustment we need for the sustainable growth and to prepare for the money going out

International monetary cooperation has broken down. Industrial countries have to play a part in restoring that, and they can’t at this point wash their hands off and say we’ll do what we need to and you do the adjustment. ….Fortunately the IMF has stopped giving this as its mantra, but you hear from the industrial countries: We’ll do what we have to do, the markets will adjust and you can decide what you want to do…. We need better cooperation and unfortunately that’s not been forthcoming so far.

Science Perspective

Researchers Discover How Traders Act Like Herds And Cause Market Bubbles

Building on similarities between earthquakes and extreme financial events, we use a self-organized criticality-generating model to study herding and avalanche dynamics in financial markets. We consider a community of interacting investors, distributed in a small-world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market which has been specified according to the S&P 500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of traders, randomly distributed inside the network, who adopt a random investment strategy. Our findings suggest a promising strategy to limit the size of financial bubbles and crashes. We also obtain that the resulting wealth distribution of all traders corresponds to the well-known Pareto power law, while that of random traders is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders. http://pre.aps.org/abstract/PRE/v88/i6/e062814

Blogs review: Getting rid of the Euler equation – the equation at the core of modern macro The Euler equation is one of the fundamentals, at a deep level, of dynamic stochastic general equilibrium (DSGE) models promoted as the latest and greatest in theoretical macroeconomics. After the general failures in mainstream macroeconomics with 2008-09, DGSE have come into question, and this review is interesting because it suggests, to my way of thinking, that the Euler equation linking past and future consumption patterns is essentially grafted onto empirical data artificially. It is profoundly in synch with neoclassical economic theory of consumer optimization, but cannot be said to be supported by the data in any robust sense. Interesting read with links to further exploration.

BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE: Revisiting the Foundations of Statistics – check this out – we need the presentations online.